Given a network property or a data structure, a local certification is a labeling that allows to efficiently check that the property is satisfied, or that the structure is correct. The quality of a certification is measured by the size of its labels: the smaller, the better.This notion plays a central role in self-stabilization, because the size of the certification is a lower bound (and often an upper bound) on the memory needed for silent self-stabilizing construction of distributed data structures. From the point of view of distributed computing in general, it is also a measure of the locality of a property (e.g. properties of the network itself, such as planarity). When it comes to the size of the certification labels, one can identify three important regimes: the properties for which the optimal size is polynomial in the number of vertices of the graph, the ones that require only polylogarithmic size, and the ones that can be certified with a constant number of bits. The first two regimes are well studied, with several upper and lower bounds, specific techniques, and active research questions. On the other hand, the constant regime has never been really explored, at least on the lower bound side. The main contribution of this paper is the first non-trivial lower bound for this low regime. More precisely, we show that by using certification on just one bit (a binary certification), one cannot certify $k$-colorability for $k\geq 3$. To do so, we develop a new technique, based on the notion of score, and both local symmetry arguments and a global parity argument. We hope that this technique will be useful for establishing stronger results. We complement this result by a discussion of the implication of lower bounds for this constant-size regime, and with an upper bound for a related problem, illustrating that in some cases one can do better than the natural upper bound.
翻译:在网络属性或数据结构中, 本地认证是一种标签, 可以有效检查属性是否满意, 或者结构是否正确。 认证的质量以其标签大小来衡量: 较小, 更好。 这个概念在自我稳定中起着核心作用, 因为认证的大小在静默构建分布式数据结构所需的记忆中是一个较低约束( 通常是上约束 ) 。 从一般分布式计算的角度来看, 它也是测量属性( 网络本身的属性, 如计划性 ) 位置的尺度 。 当认证的质量以其标签大小衡量: 较小, 更好。 这个概念在自我稳定中扮演着核心角色。 认证的大小在静态构建分布式数据结构所需的记忆中是一个较低约束( 通常是上下约束 ) 。 从上和下分布式计算的角度看, 前两个机制是用来测量属性的大小( 网络本身的属性, 如计划性 本身的属性, 如计划性等 ) 。 当涉及到认证的大小时,, 我们无法确定三个常规的属性 。 在本文中, 最终的验证结果将用这个最低的 。